Below, we step-through how the protocol guidance (Section ) can be used to model an example of a counterfactual explanation from this clinical use case.
A counterfactual explanation can be generated by modifying a factor on the treatment planning screen of the CDSS prototype.
For illustration's sake, let us suppose that this explanation is needed to address a question, “What if the patient had an ASCVD risk factor?" where the “ASCVD risk factor" is an alternate set of inputs the system had not previously considered.
From our definition of a counterfactual explanation (https://tetherless-world.github.io/explanation-ontology/modeling/#counterfactual), in response to the modification a system would need to generate a system recommendation based on the consideration of the new input. More specifically, in our use case, a system would need to consider the alternate input of the ASCVD risk factor in conjunction with the patient context and consult evidence from the American Diabetes Association (ADA) guidelines [1]} to arrive at a new suitable drug recommendation for the patient case. With the alternate set of inputs and the corresponding recommendation in place, the counterfactual explanation components can now be populated as slots based on the sufficiency condition for this explanation type. A Turtle snippet of this counterfactual explanation example can be viewed in Fig. REF . Additionally, there have been some promising machine learning (ML) model efforts in the explainability space [2]}, [3]} that could be used to generate system recommendations to populate specific explanation types. We are investigating how to integrate some of these AI methods with the semantic encoding of EO.
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